In the post-World War II era land development in the United States has largely been focused on the expansion away from urban centers and out into the surrounding suburbs. While the development of suburbs began with utopian ideals of spiritual wholeness, their actual manifestation on the American landscape has been subject to harsh critiques about their long-term economic and environmental feasibility, fostering of social alienation, and general placelessness. In this thesis I address the criticism of suburbs as placeless, asking ―What are the particular practices of place-making in North American suburbs?‖ Examining interviews, cognitive map surveys, participant observation, archival materials, and geoweb activity through lenses of imageability and anticipatory action I seek to better understand how the residents of an Indianapolis suburb narrate, structure, and produce a sense of place in their own community. In doing so I argue that that suburbs force an understanding of place as both experiential and social that is beyond mere aesthetics.

Wave-dominated and mixed tidal and wave energy barrier islands are assumed to have characteristic dune topographies that link to their macroscale form. However, there has been no systematic attempt to describe the linkage between barrier island macroscale form and dune topography. The goal of this thesis was to investigate how dune topographies correspond to a number of barrier island morphologies found along the southeastern U.S. Atlantic coast. Macroscale process-form variables were used to classify 77 islands into seven morphologic clusters. Islands from each cluster were selected and sites characteristic of the range of dune topographies within islands were characterized using three methods: the frequency distribution of elevations, directional spatial autocorrelation of elevation at different distance classes, and FRAGSTATS indices summarizing the patch structure of elevations. Variables derived from each of these methods peaked in their ability to discriminate among barrier island morphologies when the islands were aggregated into three groups. An ordination of those variables revealed a two or three-fold grouping of barrier island dune types that approximated the traditional wave dominated and mixed energy barrier island morphologic classification. These findings suggest that dune topographies converge upon two to three configurations even within the heterogeneity in macroscale island morphology.

Station siting, exposure, instrumentation, and time of observations influence longterm climatic records. This thesis compared and analyzed temperature data from four Kentucky Mesonet stations located in Fayette (LXGN), Franklin (LSML), Clark (WNCH), and Bullitt (CRMT) counties to two nearby Automated Surface Observation Systems (ASOS) stations in Kentucky. The ASOS stations are located at Louisville International Airport (Standiford Field - KSDF) and at Lexington Airport (Blue Grass Field - KLEX). The null hypothesis states that there is no significant difference in temperature measurements between the two types of stations. To quantify the differences in temperature measurements, geoprofiles and the following statistical procedures were used: coefficient of determination (R2), coefficient of efficiency (E), index of agreement (d), root mean square error (RMSE), and mean absolute error (MAE). Geoprofiles were developed using GIS, and take into account elevation, slope, hillshading, land use, and aspect for each site to help better understand the influence of local topography. It was found that temperature differences could be related to the advancement of weather patterns, vegetation growth and decay, and changes in the landscape at the stations. KSDF consistently recorded higher temperatures than those at CRMT. The positive bias ranged between 0.27 and 2.41 ºC during the time period of September 2009 to August 2010. KLEX was found to be warmer or cooler, with temperature differences that ranged from -1.42 to 0.22 ºC for LXGN, LSML, and WNCH. The index of agreement at KSDF for mean hourly temperatures, when compared to the Bullitt County mesonet station, ranged from 0.88 to 0.99. Meanwhile, the index of agreement at KLEX was 0.96 to 1.00 when compared to the Franklin, Fayette, and Clark mesonet stations. KLEX recorded temperatures that were higher or lower compared to the Franklin, Fayette, and Clark mesonet stations. At the seasonal scale, fall and summer showed larger differences between the Mesonet and ASOS observations. KSDF consistently recorded higher temperatures ranging up to 2.41 °C during the summer. The index of agreement at KSDF in the fall, when compared to the Bullitt County mesonet station average temperatures, ranged from 0.89 to 0.95, while in the summer it was 0.88 to 0.96. The d index indicates a good agreement between ASOS and mesonet stations in winter. KLEX indicates that the index of agreement, RMSE, and MAE are best during winter for all three stations, while in the fall and summer the agreement was not as strong when compared to the Franklin, Fayette, and Clark mesonet stations. In summary, results indicate that the Kentucky Mesonet and ASOS temperature measurements show significant differences throughout the year; therefore, the alternative hypothesis is accepted. These differences are attributed to biases associated with ASOS observations, nearby artificial sources of heating, equipment/maintenance procedures, and land use and land cover at the site.

In California, voters frequently face ballot propositions dealing directly or indirectly with environmental protection. Records of these votes provide powerful evidence of the character of voters’ demand and willingness-to-pay for environmental public goods (e.g., air quality, watershed ecosystem services, parks and recreation), and have been used in past environmental econometrics research to produce aggregated income and price effect estimates. Using neighborhood-level voting records on seven environmental-related ballot propositions in California between 2002 and 2010, this econometric study investigates the nature of voters’ demand for environmental public goods, focusing on the effect of household income on pro-environment voting. Unlike previous studies, this study uses geographically weighted regression (GWR) to determine how estimates vary across the historically, culturally, and politically diverse state of California. Preliminary statewide results from an ordinary least-squares regression model suggest that demand decreases with voter income, and that this negative income effect is strongest among lower-income households. However, GWR results suggest that the magnitude, and even the sign, of income effects varies regionally. The San Francisco Bay Area, in particular, stands out as anomalous from the statewide model estimates: in this region, wealthier households are more likely than lower-income households to support environmental propositions, ceteris paribus. This finding is consistent across all propositions studied, which include water bonds, State Parks funding, and the California High-Speed Rail program, among others. GWR results suggest that political geography and regional culture determines the way in which income (as well as education and other factors) affects voters’ support of environmental propositions.

Although major whaling practices have ceased, increasing human involvement and influence in the world’s marine ecosystems continue to adversely effect global whale populations. It is a major concern throughout Antarctic waters, where endangered Oceania Humpback Whales (Megaptera novarangliae) annually feed. This study analyzes the extent to which a proposed marine protected area within the Ross Sea may indirectly harm the last remaining endangered population of Humpbacks. Using current satellite tracks of southern Humpback migrations, this model maps the effects of displaced Toothfish fisheries, and suggests further conservations efforts, based on New Zealand’s Precautionary Approach, to protect these vulnerable whales.

Despite being an indicator of modernization and macro-economic growth, urbanization in regions such as Sub-Saharan Africa is tightly interwoven with poverty and deprivation. This has manifested physically as slums, which represent the worst residential urban areas, marked by lack of access to good quality housing and basic services. To effectively combat the slum phenomenon, local slum conditions must be captured in quantitative and spatial terms. However, there are significant hurdles to this. Slum detection and mapping requires readily available and reliable data, as well as a proper conceptualization of measurement and scale. Using Bamenda, Cameroon, as a test case, this dissertation research was designed as a three-pronged attack on the slum mapping problematic. The overall goal was to investigate locally optimized slum mapping strategies and methods that utilize high resolution satellite image data, household survey data, simple machine learning and regionalization theory. The first major objective of the study was to tackle a "measurement" problem. The aim was to explore a multi-index approach to measure and map local slum conditions. The rationale behind this was that prior sub-Saharan slum research too often used simplified measurement techniques such as a single unweighted composite index to represent diverse local slum conditions. In this study six household indicators relevant to the United Nations criteria for defining slums were extracted from a 2013 Bamenda household survey data set and aggregated for 63 local statistical areas. The extracted variables were the percent of households having the following attributes: more than two residents per room, non-owner, occupying a single room or studio, having no flush toilet, having no piped water, having no drainage. Hierarchical variable clustering was used as a surrogate for exploratory factor analysis to determine fewer latent slum factors from these six variables. Variable groups were classified such that the most correlated variables fell in the same group while non-correlated variables fell in separate groups. Each group membership was then examined to see if the group suggested a conceptually meaningful slum factor which could quantified as a stand-alone "high" and "low" binary slum index. Results showed that the slum indicators in the study area could be replaced by at least two meaningful and statistically uncorrelated latent factors. One factor reflected the home occupancy conditions (tenancy status, overcrowded and living space conditions) and was quantified using K-means clustering of units as an ‘occupancy disadvantage index’ (Occ_D). The other reflected the state of utilities access (piped water and flush toilet) and was quantified as utilities disadvantage index (UT_D). Location attributes were used to examine/validate both indices. Independent t-tests showed that units with high Occ_D were on average closer to nearest town markets and major roads when compared with units of low Occ_D. This was consistent with theory as it is expected that typical slum residents (in this case overcrowded and non-owner households) will favor accessibility to areas of high economic activity. However, this situation was not the same with UT_D as shown by lack of such as a strong pattern. The second major objective was to tackle a "learning" problem. The purpose was to explore the potential of unsupervised machine learning to detect or "learn" slum conditions from image data. The rationale was that such an approach would be efficient, less reliant on prior knowledge and expertise. A 2012 GeoEye image scene of the study area was subjected to image classification from which the following physical settlement attributes were quantified for each of the 63 statistical areas: per cent roof area, percent open space area, per cent bare soil, per cent paved road surface, per cent dirt road surface, building shadow-roof area ratio. The shadow-roof ratio was an innovative measure used to capture the size and density attributes of buildings. In addition to the 6 image derived variables, the mean slope of each area was calculated from a digital elevation dataset. All 7 attributes were subject to principal component analysis from which the first 2 components were extracted and used for hierarchical clustering of statistical areas to derive physical types. Results show that area units could be optimally classified into 4 physical types labelled generically as Categories 1 – 4, each with at least one defining physical characteristic. Kruskal Wallis tests comparing physical types in terms of household and locations attributes showed that at least two physical types were different in terms of aggregated household slum conditions and location attributes. Category 4 areas, located on steep slopes and having high shadow-to-roof ratio, had the highest distribution of non-owner households. They were also located close to nearest town markets. They were thus the most likely candidates of slums in the city. Category 1 units on other hand located at the outskirts and having abundant open space were least likely to have slum conditions. The third major objective was to tackle the problem of "spatial scale". Neighborhoods, by their very nature of contiguity and homogeneity, represent an ideal scale for urban spatial analysis and mapping. Unfortunately, in most areas, neighborhoods are not objectively defined and slum mapping often relies in the use of arbitrary spatial units which do not capture the true extent of the phenomenon. The objective was thus to explore the use of analytic regionalization to quantitatively derive the neighborhood unit for mapping slums. Analytic neighborhoods were created by spatially constrained clustering of statistical areas using the minimum spanning tree algorithm. Unlike previous studies that relied on socio-economic and/or demographic information, this study innovatively used multiple land cover and terrain attributes as neighborhood homogenizing factors. Five analytic neighborhoods (labeled Regions 1-5) were created this way and compared using Kruskal Wallis tests for differences in household slum attributes. This was to determine largest possible contiguous areas that could be labeled as slum or non-slum neighborhoods. The results revealed that at least two analytic regions were significantly different in terms of aggregated household indicators. Region 1 stood apart as having significantly higher distributions of overcrowded and non-owner households. It could thus be viewed as the largest potential slum neighborhood in the city. In contrast, regions 3 (located at higher elevation and separated from rest of city by a steep escarpment) was generally associated with low distribution of household slum attributes and could be considered the strongest model of a non-slum or formal neighborhood. Both Regions 1 and 3 were also qualitatively correlated with two locally recognized (vernacular) neighborhoods. These neighborhoods, "Sisia" (for Region 1) and "Up Station" (for Region 3), are commonly perceived by local folk as occupying opposite ends of the socio-economic spectrum. The results obtained by successfully carrying the three major objectives have major implication for future research and policy. In the case of multi-index analysis of slum conditions, it affirms the notion the that slum phenomenon is diverse in the local context and that remediation efforts must be compartmentalized to be effective. The results of image based unsupervised mapping of slums from imagery show that it is a tool with high potential for rapid slum assessment even when there is no supporting field data. Finally, the results of analytic regionalization showed that the true extent of contiguous slum neighborhoods can be delineated objectively using land cover and terrain attributes. It thus presents an opportunity for local planning and policy actors to consider redesigning the city neighborhood districts as analytic units. Quantitively derived neighborhoods are likely to be more useful in the long term, be it for spatial sampling, mapping or planning purposes.

Many tools exist to assist law enforcement agencies in mitigating criminal activity. For centuries, academics used statistics in the study of crime and criminals, and more recently, police departments make use of spatial statistics and geographic information systems in that pursuit. Clustering and hot spot methods of analysis are popular in this application for their relative simplicity of interpretation and ease of process. With recent advancements in geospatial technology, it is easier than ever to publicly share data through visual communication tools like web applications and dashboards. Sharing data and results of analyses boosts transparency and the public image of police agencies, an image important to maintaining public trust in law enforcement and active participation in community safety.

Stellar wind bow shocks are structures created when stellar winds with supersonic relative velocities interact with the local interstellar medium (ISM). They can be studied to understand the properties of stars as well as the ISM. Since bow shocks are asymmetric, light becomes polarized by scattering in the regions of enhanced density they create. We use a Monte Carlo radiative transfer code calle SLIP to simulate the polarization signatures produced by both resolved and unresolved bow shocks with analytically derived shapes and density structures. When electron scattering is the polarizing mechanism, we find that optical depth plays an important role in the polarization signatures. While results for low optical depths reproduce theoretical predictions, higher optical depths produce higher polarization and position angle rotations at specific viewing angles. This is due to the geometrical properties of the bow shock along with multiple scattering effects. For dust scattering, we find that the polarization signature is strongly affected by wavelength, dust size, dust composition, and viewing angle. Depending on the viewing angle, the polarization magnitude may increase or decrease as a function of wavelength. We will present results from these simulations and preliminary comparisons with observational data.

A geophysical survey was conducted at David Crockett Birthplace State Park (40GN205, 40GN12) using ground-penetrating radar (GPR) and magnetometry. The data indicated multiple levels of occupation that were investigated by Phase II and Phase III archaeological excavations. New cultural components were discovered, including the remnants of a Protohistoric Native American structure containing European glass trade beads and Middle Woodland artifacts that suggest trade with Hopewell groups from Ohio. A circular Archaic hearth was uncovered at one meter below surface and similar deep anomalies were seen in the GPR data at this level. A semi-automated object-based image analysis (OBIA) was implemented to extract Archaic circular hearths from GPR depth slices using user-defined spatial parameters (depth, area, perimeter, length to width ratio, and circularity index) followed by manual interpretation. This approach successfully identified sixteen probable hearths distributed across the site in a semi-clustered pattern.

Understanding the composition of lotic communities and the landscape processes and habitat characteristics that shape them is one of the main challenges confronting stream ecologists. In order to better understand the linkages among landscape processes, stream habitat, and biological communities and to understand how accurately our measurements represent important factors influencing biological communities, it is important to test explicit hypotheses regarding these linkages. Increasing our understanding of aquatic communities in a hierarchical context and recognizing how well our measurements represent factors structuring aquatic communities will help managers better evaluate the influence of land management practices on aquatic ecosystems, direct conservation strategies, and lead to better assessments of ecological condition. In Chapter 2, we used spatial data, field-based habitat measurements, and macroinvertebrate community data to 1) examine the influence of landscape processes on two factors of stream habitat; maximum stream temperatures and fine sediment, and to 2) examine how well these landscape and habitat characteristics represent factors influencing gradients in macroinvertebrate community structure. The results of this study showed that spatially derived measurements may be effectively used to test hypotheses regarding landscape influences on stream habitat and that spatial data, used in conjunction with field measurements can provide important information regarding factors influencing gradients in biological communities. In addition, spatially derived measurements may provide the same or additional information regarding influences on community structure as field-based measurements, which suggests that further research should be done to assess how well our field measurements represent factors that are important in shaping stream communities. The objective of Chapter 3 was to compare how well single field measurements and a combination of indicator variables hypothesized to be components of a single ecological processes or concept, known as a latent variable, represent thermal stress and fine sediment influences on macroinvertebrate communities. Results from this study showed that both single and latent variables explained relatively the same amount of variation in macroinvertebrate community structure. This suggests that while latent variables may have a potential to better refine how we represent ecological factors, a better basis for defining a priori hypotheses is needed before these variables can provide any additional information compared to single habitat measurements.